基于受约束偏置的概率矩阵分解算法

在概率矩阵分解(PMF)模型拟合之后,评分较少用户的特征趋近于先验分布的平均值,导致对其评分预测接近物品的平均评分.受约束概率矩阵分解(CPMF)未考虑到不同评分系统的整体差异以及数据集内部用户与物品存在的固有属性.针对以上问题,提出将传统矩阵分解中的用户和物品偏置项以及全局平均分结合受约束概率矩阵分解来建立新的矩阵分解算法.算法利用整体平均分衡量不同评分系统,在采用偏置来表示用户以及物品之间相互独立的属性的同时,引入约束使行为相近用户拥有相近的用户偏置,从而提高预测精度.在两个真实数据集上的实验结果表明,该算法相对于PMF和CPMF算法预测精度得到了提高.

Probabilistic Matrix Factorization Based on Constrained Bias

Users who rate few will have features that are close to the prior mean once the probabilistic matrix factorization(PMF) model has been fitted, which leads to the predictions close to the movie average ratings. Constrained probabilistic matrix factorization(CPMF) algorithm has not fully considered about the holistic diversity among different rating systems and the inherent attributes that different users and products hold within the datasets. To solve the problems above, the user and product bias and global average are combined with constrained probabilistic matrix factorization to build a new matrix factorization algorithm. The algorithm brings in the constrains to restrain the user bias among users of similar action while evaluating different rating systems with global average and representing the attributes of different users and products with biases to increase the prediction accuracy. The results of experiments on two real datasets indicate that the prediction accuracy of the algorithm has been improved compared to PMF and CPMF.